The present invention relates to methods and devices used to detect irregularities in the beating of an animal heart, generally known as “arrhythmias.” More particularly, the invention relates to a method and an apparatus to detect atrial fibrillation (“AF”).
As is well known in the medical field, the contraction of an animal heart is controlled by a series of electrical signals that originate in the sinus node of the right atrium. These signals can be recorded and the record can be used as a diagnostic and treatment tool. An electrocardiogram (“ECG”) is a graphic display of the electrical signals that cause the heart to contract. A representative ECG waveform 10 is shown in FIG. 1. The ECG 10 includes a number of crests (generally referred to as “waves”) and a number of troughs. A normal ECG, such as the ECG waveform 10, includes a P wave, which represents the electrical potential generated as atrial cells in the heart depolarize before contraction. Following the P wave is a crest and trough combination known as the QRS complex, which includes Q, R, and S waves. The QRS complex is caused by the electrical potential generated when the ventricular muscle cells depolarize before contraction. Following the QRS complex is a T wave. The T wave is caused by electrical potential generated as the ventricles of the heart recover from the state of the polarization. Following the T wave is a short period of relative inactivity. A new contraction begins with a second P wave, P2.
A variety of methods and devices have been developed to assist physicians in interpreting ECGs. One such tool is known as EKpro detection software, which is available from GE Medical Systems Information Technologies, Inc., the assignee of the present application. EKpro software is designed for monitoring ECG signals in adults and paced patients. EKpro software has particular application in detecting atrial fibrillation. AF is identified by an irregular heart rhythm and is clinically defined as uncoordinated contractions of the atria. The ECG of a patient suffering from atrial fibrillation typically demonstrates irregular ventricular contractions and the absence of P waves. If allowed to continue, AF can cause decreases in exercise tolerance and left ventricular function. In more severe cases, AF can lead to a fatal medical condition.
The problems associated with AF can be reversed if sinus rhythm can be restored. The identification of AF allows a caregiver to administer a treatment to control symptoms and to prevent more serious complications. Most often, the treatment is specific to the nature of the atrial fibrillation suffered by the patient and, in particular, the heart rate of the patient, the symptoms suffered by the patient, and the duration of the AF events. Some current software systems attempt to detect AF based on ventricular activity. However, these types of systems can, in general, only suggest that an irregular rhythm may be caused by atrial fibrillation. One version of the EKpro product mentioned above is used in Holter monitoring, and determines the probability of an AF condition using a Hidden Markov Model (“HMM”) methodology. While the EKpro product is better than many others in predicting an AF condition, it still suffers from the problem of relying solely on ventricular data, and is dependent upon the data used to train it. In order to provide accurate predictions a complete data set covering all irregular heart rhythms, not just AF, is required. Since such data sets are often difficult to compile, a system solely dependent on a HMM methodology can be biased.
Another difficulty with present systems is that many are not, in general, able to specifically identify AF over other irregular rhythms. The inability to specifically identify the exact irregular rhythm suffered by the patient is problematic in hospital environments, where most patient monitoring is alarm-based. That is, whenever any irregular condition is detected, an alarm is set off. When an alarm sounds, a medical professional must respond. The medical professional can assess the situation and identify if the alarm is true or false. If there are too many false alarms, the medical professional may become desensitized, responding slowly or lackadaisically to alarms. This may cause a professional to respond inadequately when a severe or critical condition occurs.